OpenRaiser/Intern-Atlas huggingface dataset:
GPU-Based Parallel Integration of Large Numbers of Independent ODE Systems
Venue: arXiv cs.MS 2016
Year: 2016

The task of integrating a large number of independent ODE systems arises in various scientific and engineering areas. For nonstiff systems, common explicit integration algorithms can be used on GPUs, where individual GPU threads concurrently integrate independent ODEs with different initial conditions or parameters. One example is the fifth-order adaptive Runge-Kutta-Cash-Karp (RKCK) algorithm. In the case of stiff ODEs, standard explicit algorithms require impractically small time-step sizes for stability reasons, and implicit algorithms are therefore commonly used instead to allow larger time steps and reduce the computational expense. However, typical high-order implicit algorithms based on backwards differentiation formulae (e.g., VODE, LSODE) involve complex logical flow that causes severe thread divergence when implemented on GPUs, limiting the performance. Therefore, alternate algorithms are needed. A GPU-based Runge-Kutta-Chebyshev (RKC) algorithm can handle moderate levels of stiffness and performs significantly faster than not only an equivalent CPU version but also a CPU-based implicit algorithm (VODE) based on results shown in the literature. In this chapter, we present the mathematical background, implementation details, and source code for the RKCK and RKC algorithms for use integrating large numbers of independent systems of ODEs on GPUs. In addition, brief performance comparisons are shown for each algorithm, demonstrating the potential benefit of moving to GPU-based ODE integrators.
Balotage in Argentina 2015, a sentiment analysis of tweets
Venue: arXiv cs.IR 2016
Year: 2016

Twitter social network contains a large amount of information generated by its users. That information is composed of opinions and comments that may reflect trends in social behavior. There is talk of trend when it is possible to identify opinions and comments geared towards the same shared by a lot of people direction. To determine if two or more written opinions share the same address, techniques Natural Language Processing (NLP) are used. This paper proposes a methodology for predicting reflected in Twitter from the use of sentiment analysis functions NLP based on social behaviors. The case study was selected the 2015 Presidential in Argentina, and a software architecture Big Data composed Vertica data base with the component called Pulse was used. Through the analysis it was possible to detect trends in voting intentions with regard to the presidential candidates, achieving greater accuracy in predicting that achieved with traditional systems surveys.
PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks
Venue: arXiv cs.AR 2016
Year: 2016

Convolutional neural networks (CNNs) have been widely employed in many applications such as image classification, video analysis and speech recognition. Being compute-intensive, CNN computations are mainly accelerated by GPUs with high power dissipations. Recently, studies were carried out exploiting FPGA as CNN accelerator because of its reconfigurability and energy efficiency advantage over GPU, especially when OpenCL-based high-level synthesis tools are now available providing fast verification and implementation flows. Previous OpenCL-based design only focused on creating a generic framework to identify performance-related hardware parameters, without utilizing FPGA's special capability of pipelining kernel functions to minimize memory bandwidth requirement. In this work, we propose an FPGA accelerator with a new architecture of deeply pipelined OpenCL kernels. Data reuse and task mapping techniques are also presented to improve design efficiency. The proposed schemes are verified by implementing two representative large-scale CNNs, AlexNet and VGG on Altera Stratix-V A7 FPGA. We have achieved a similar peak performance of 33.9 GOPS with a 34% resource reduction on DSP blocks compared to previous work. Our design is openly accessible and thus can be reused to explore new architectures for neural network accelerators.
On the Interplay of Link-Flooding Attacks and Traffic Engineering
Venue: arXiv cs.NI 2016
Year: 2016

Link-flooding attacks have the potential to disconnect even entire countries from the Internet. Moreover, newly proposed indirect link-flooding attacks, such as 'Crossfire', are extremely hard to expose and, subsequently, mitigate effectively. Traffic Engineering (TE) is the network's natural way of mitigating link overload events, balancing the load and restoring connectivity. This work poses the question: Do we need a new kind of TE to expose an attack as well? The key idea is that a carefully crafted, attack-aware TE could force the attacker to follow improbable traffic patterns, revealing his target and his identity over time. We show that both existing and novel TE modules can efficiently expose the attack, and study the benefits of each approach. We implement defense prototypes using simulation mechanisms and evaluate them extensively on multiple real topologies.
Bots, Seeds and People: Web Archives as Infrastructure
Venue: arXiv cs.DL 2016
Year: 2016

The field of web archiving provides a unique mix of human and automated agents collaborating to achieve the preservation of the web. Centuries old theories of archival appraisal are being transplanted into the sociotechnical environment of the World Wide Web with varying degrees of success. The work of the archivist and bots in contact with the material of the web present a distinctive and understudied CSCW shaped problem. To investigate this space we conducted semi-structured interviews with archivists and technologists who were directly involved in the selection of content from the web for archives. These semi-structured interviews identified thematic areas that inform the appraisal process in web archives, some of which are encoded in heuristics and algorithms. Making the infrastructure of web archives legible to the archivist, the automated agents and the future researcher is presented as a challenge to the CSCW and archival community.
The Loss Surface of Residual Networks: Ensembles and the Role of Batch Normalization
Venue: arXiv cs.CV 2016
Year: 2016

Deep Residual Networks present a premium in performance in comparison to conventional networks of the same depth and are trainable at extreme depths. It has recently been shown that Residual Networks behave like ensembles of relatively shallow networks. We show that these ensembles are dynamic: while initially the virtual ensemble is mostly at depths lower than half the network's depth, as training progresses, it becomes deeper and deeper. The main mechanism that controls the dynamic ensemble behavior is the scaling introduced, e.g., by the Batch Normalization technique. We explain this behavior and demonstrate the driving force behind it. As a main tool in our analysis, we employ generalized spin glass models, which we also use in order to study the number of critical points in the optimization of Residual Networks.
LTL Model-Checking for Dynamic Pushdown Networks Communicating via Locks
Venue: arXiv cs.LO 2016
Year: 2016

A Dynamic Pushdown Network (DPN) is a set of pushdown systems (PDSs) where each process can dynamically create new instances of PDSs. DPNs are a natural model of multi-threaded programs with (possibly recursive) procedure calls and thread creation. Extending DPNs with locks allows processes to synchronize with each other. Thus, DPNs with locks are a well adapted formalism to model multi-threaded programs that synchronize via locks. Therefore, it is important to have model-checking algorithms for DPNs with locks. We consider in this work model-checking for DPNs with locks against single-indexed LTL properties of the form V fi s.t. fi is a LTL formula interpreted over the PDS i. We consider the model-checking problems w.r.t. simple valuations (i.e, whether a configuration satisfies an atomic proposition depends only on its control location and held locks) and w.r.t. regular valuations (i.e., the set of the configurations satisfying an atomic proposition is a regular set of configurations). We show that these model-checking problems are decidable.
Analysis of Static Cellular Cooperation between Mutually Nearest Neighboring Nodes
Venue: arXiv cs.NI 2016
Year: 2016

Cooperation in cellular networks is a promising scheme to improve system performance. Existing works consider that a user dynamically chooses the stations that cooperate for his/her service, but such assumption often has practical limitations. Instead, cooperation groups can be predefined and static, with nodes linked by fixed infrastructure. To analyze such a potential network, we propose a grouping method based on node proximity. With the Mutually Nearest Neighbour Relation, we allow the formation of singles and pairs of nodes. Given an initial topology for the stations, two new point processes are defined, one for the singles and one for the pairs. We derive structural characteristics for these processes and analyse the resulting interference fields. When the node positions follow a Poisson Point Process (PPP) the processes of singles and pairs are not Poisson. However, the performance of the original model can be approximated by the superposition of two PPPs. This allows the derivation of exact expressions for the coverage probability. Numerical evaluation shows coverage gains from different signal cooperation that can reach up to 15% compared to the standard noncooperative coverage. The analysis is general and can be applied to any type of cooperation in pairs of transmitting nodes.
Mechanical Novel: Crowdsourcing Complex Work through Reflection and Revision
Venue: arXiv cs.HC 2016
Year: 2016

Crowdsourcing systems accomplish large tasks with scale and speed by breaking work down into independent parts. However, many types of complex creative work, such as fiction writing, have remained out of reach for crowds because work is tightly interdependent: changing one part of a story may trigger changes to the overall plot and vice versa. Taking inspiration from how expert authors write, we propose a technique for achieving interdependent complex goals with crowds. With this technique, the crowd loops between reflection, to select a high-level goal, and revision, to decompose that goal into low-level, actionable tasks. We embody this approach in Mechanical Novel, a system that crowdsources short fiction stories on Amazon Mechanical Turk. In a field experiment, Mechanical Novel resulted in higher-quality stories than an iterative crowdsourcing workflow. Our findings suggest that orienting crowd work around high-level goals may enable workers to coordinate their effort to accomplish complex work.
Automatic recognition of child speech for robotic applications in noisy environments
Venue: arXiv cs.CL 2016
Year: 2016

Automatic speech recognition (ASR) allows a natural and intuitive interface for robotic educational applications for children. However there are a number of challenges to overcome to allow such an interface to operate robustly in realistic settings, including the intrinsic difficulties of recognising child speech and high levels of background noise often present in classrooms. As part of the EU EASEL project we have provided several contributions to address these challenges, implementing our own ASR module for use in robotics applications. We used the latest deep neural network algorithms which provide a leap in performance over the traditional GMM approach, and apply data augmentation methods to improve robustness to noise and speaker variation. We provide a close integration between the ASR module and the rest of the dialogue system, allowing the ASR to receive in real-time the language models relevant to the current section of the dialogue, greatly improving the accuracy. We integrated our ASR module into an interactive, multimodal system using a small humanoid robot to help children learn about exercise and energy. The system was installed at a public museum event as part of a research study where 320 children (aged 3 to 14) interacted with the robot, with our ASR achieving 90% accuracy for fluent and near-fluent speech.
Deep Convolutional Neural Network for 6-DOF Image Localization
Venue: arXiv cs.CV 2016
Year: 2016

We present an accurate and robust method for six degree of freedom image localization. There are two key-points of our method, 1. automatic immense photo synthesis and labeling from point cloud model and, 2. pose estimation with deep convolutional neural networks regression. Our model can directly regresses 6-DOF camera poses from images, accurately describing where and how it was captured. We achieved an accuracy within 1 meters and 1 degree on our out-door dataset, which covers about 2 acres on our school campus.
Arb: Efficient Arbitrary-Precision Midpoint-Radius Interval Arithmetic
Venue: arXiv cs.MS 2016
Year: 2016

Arb is a C library for arbitrary-precision interval arithmetic using the midpoint-radius representation, also known as ball arithmetic. It supports real and complex numbers, polynomials, power series, matrices, and evaluation of many special functions. The core number types are designed for versatility and speed in a range of scenarios, allowing performance that is competitive with non-interval arbitrary-precision types such as MPFR and MPC floating-point numbers. We discuss the low-level number representation, strategies for precision and error bounds, and the implementation of efficient polynomial arithmetic with interval coefficients.
Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest
Venue: arXiv cs.CV 2016
Year: 2016

Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved.
A Public Comment on NCCoE's White Paper on Privacy-Enhancing Identity Brokers
Venue: arXiv cs.CR 2016
Year: 2016

The National Cybersecurity Center of Excellence (NCCoE) (in the United States) has published on October 19, 2015, a white paper on "privacy-enhanced identity brokers." We present here a reply to their request for public comments. We enumerate concerns whose consideration we find paramount for the design of a privacy-enhancing identity brokering solution, for identification and authentication of citizens into myriad online services, and we recommend how to incorporate them into a revised white paper. Our observations, focused on privacy, security, auditability and forensics, are mostly based on a recently published research paper (PETS 2015) about two nation-scale brokered identification systems.
Bayesian data assimilation based on a family of outer measures
Venue: arXiv cs.IT 2016
Year: 2016

A flexible representation of uncertainty that remains within the standard framework of probabilistic measure theory is presented along with a study of its properties. This representation relies on a specific type of outer measure that is based on the measure of a supremum, hence combining additive and highly sub-additive components. It is shown that this type of outer measure enables the introduction of intuitive concepts such as pullback and general data assimilation operations.
Privacy-Preserving Genetic Relatedness Test
Venue: arXiv cs.CR 2016
Year: 2016

An increasing number of individuals are turning to Direct-To-Consumer (DTC) genetic testing to learn about their predisposition to diseases, traits, and/or ancestry. DTC companies like 23andme and Ancestry.com have started to offer popular and affordable ancestry and genealogy tests, with services allowing users to find unknown relatives and long-distant cousins. Naturally, access and possible dissemination of genetic data prompts serious privacy concerns, thus motivating the need to design efficient primitives supporting private genetic tests. In this paper, we present an effective protocol for privacy-preserving genetic relatedness test (PPGRT), enabling a cloud server to run relatedness tests on input an encrypted genetic database and a test facility's encrypted genetic sample. We reduce the test to a data matching problem and perform it, privately, using searchable encryption. Finally, a performance evaluation of hamming distance based PP-GRT attests to the practicality of our proposals.
Intrusion Detection System for Applications using Linux Containers
Venue: arXiv cs.CR 2016
Year: 2016

Linux containers are gaining increasing traction in both individual and industrial use, and as these containers get integrated into mission-critical systems, real-time detection of malicious cyber attacks becomes a critical operational requirement. This paper introduces a real-time host-based intrusion detection system that can be used to passively detect malfeasance against applications within Linux containers running in a standalone or in a cloud multi-tenancy environment. The demonstrated intrusion detection system uses bags of system calls monitored from the host kernel for learning the behavior of an application running within a Linux container and determining anomalous container behavior. Performance of the approach using a database application was measured and results are discussed.
Tactics and Tallies: Inferring Voter Preferences in the 2016 U.S. Presidential Primaries Using Sparse Learning
Venue: arXiv cs.SI 2016
Year: 2016

In this paper, we propose a web-centered framework to infer voter preferences for the 2016 U.S. presidential primaries. Using Twitter data collected from Sept. 2015 to March 2016, we first uncover the tweeting tactics of the candidates and then exploit the variations in the number of 'likes' to infer voters' preference. With sparse learning, we are able to reveal neutral topics as well as positive and negative ones. Methodologically, we are able to achieve a higher predictive power with sparse learning. Substantively, we show that for Hillary Clinton the (only) positive issue area is women's rights. We demonstrate that Hillary Clinton's tactic of linking herself to President Obama resonates well with her supporters but the same is not true for Bernie Sanders. In addition, we show that Donald Trump is a major topic for all the other candidates, and that the women's rights issue is equally emphasized in Sanders' campaign as in Clinton's.
Noise reduction combining microphone and piezoelectric device
Venue: arXiv cs.SD 2016
Year: 2016

It is often required to extract the sound of an objective instrument played in concert with other instruments. Microphone array is one of the effective ways to enhance a sound from a specific direction. However it is not effective in an echoic room such as concert hall. The pickup microphone attached on the specific musical instrument is often employed to obtain the sound exclusively from other instrumental sounds. The obtained timbre differ from the one we hear at the usual listening position. The purpose of this paper is to propose a new method of sound separation that utilizes the piezoelectric device attached on the body of the instrument. The signal from the attached device has a different spectrum from the sound heard by the audience but has the same frequency components as the instrumental sound. Our idea is to use the device signal as a modifier of the sound focusing filter applied to the microphone sound at the listening position. The proposed method firstly estimates the frequency components of the signal from the piezoelectric device. The frequency characteristics for filtering the microphone sound are changed so that it pass the estimated frequency components. Thus we can extractthe target sound without distortion. The proposed method is a sort of dynamic sparseness approach. It was found that SNR is improved by 8.7dB through the experiments.
Multiple Service Chain Placement and Routing in a Network-enabled Cloud
Venue: arXiv cs.NI 2016
Year: 2016

Network Function Virtualization (NFV) aims to abstract the functionality of traditional proprietary hardware into software as Virtual Network Functions (VNFs), which can run on commercial off the shelf (COTS) servers. Besides reducing dependency on proprietary support, NFV helps network operators to deploy multiple services in a agile fashion. Service deployment involves placement and in sequence routing through VNFs comprising a Service Chain (SC). Our study is the first to focus on the computationally complex problem of multiple VNF SC placement and routing while considering VNF service chaining explicitly. We propose a novel column generation model for placing multiple VNF SCs and routing, which reduces the computational complexity of the problem significantly. Our aim here is to determine the ideal NFV Infrastructure (NFVI) for minimizing network resource consumption. Our results indicate that a Network enabled Cloud (NeC) results in lower networkresource consumption than a centralized NFVI (e.g., Data Center) while avoiding infeasibility with a distributed NFVI.
Ultimate tensorization: compressing convolutional and FC layers alike
Venue: arXiv cs.LG 2016
Year: 2016

Convolutional neural networks excel in image recognition tasks, but this comes at the cost of high computational and memory complexity. To tackle this problem, [1] developed a tensor factorization framework to compress fully-connected layers. In this paper, we focus on compressing convolutional layers. We show that while the direct application of the tensor framework [1] to the 4-dimensional kernel of convolution does compress the layer, we can do better. We reshape the convolutional kernel into a tensor of higher order and factorize it. We combine the proposed approach with the previous work to compress both convolutional and fully-connected layers of a network and achieve 80x network compression rate with 1.1% accuracy drop on the CIFAR-10 dataset.
Real Time Video Analysis using Smart Phone Camera for Stroboscopic Image
Venue: arXiv cs.CV 2016
Year: 2016

Motion capturing and there by segmentation of the motion of any moving object from a sequence of continuous images or a video is not an exceptional task in computer vision area. Smart-phone camera application is an added integration for the development of such tasks and it also provides for a smooth testing. A new approach has been proposed for segmenting out the foreground moving object from the background and then masking the sequential motion with the static background which is commonly known as stroboscopic image. In this paper the whole process of the stroboscopic image construction technique has been clearly described along with some necessary constraints which is due to the traditional problem of estimating and modeling dynamic background changes. The background subtraction technique has been properly estimated here and number of sequential motion have also been calculated with the correlation between the motion of the object and its time of occurrence. This can be a very effective application that can replace the traditional stroboscopic system using high end SLR cameras, tripod stand, shutter speed control and position etc.